We consider data sets describing contacts between individuals
with temporal resolution,
collected by the SocioPatterns collaboration (http://www.sociopatterns.org) in different settings:
a workplace (office building, InVS15) [1]}, a high school (Thiers13) [2]},
a primary school (LyonSchool) [3]}
and a scientific conference (SFHH) [4]}.
These data describe close face-to-face proximity of
individuals equipped with wearable sensors, with a temporal resolution of 20 seconds. To limit the effect of noise, the data are moreover often aggregated over a coarser resolution of \(\Delta \)  minutes (e.g., in kobayashi2019structural backbones are considered for \(\Delta \)  ranging from 3 to 15 minutes). Here we will use \(\Delta = 3\)  minutes, but we have obtained similar results
for other temporal resolutions.
Such data are conveniently represented as temporal networks in which nodes represent individuals.
These networks are in discrete time,
i.e., composed by \(T\)  successive snapshots at times \(t_0\) , \(t_0+\Delta , \cdots \) ,
where \(t_0\)  is the initial time of the data set.
A temporal edge between two nodes \(i\)  and \(j\)  at time \(t=t_0+n*\Delta \)  represents the fact that the corresponding
nodes have been in contact during the time interval \([t,t+\Delta ]\) . We also define a “contact” between \(i\)  and \(j\)  as an uninterrupted series of timestamps in which
there is a temporal edge between them. The duration of the contact is the length of this series.
In each case, we also define the aggregated network as the static weighted network in which a link between two nodes denotes that these two nodes have been in contact at least once, and the weight of the link is given by the number of temporal edges between these nodes.
Table REF  gives the main characteristics of each data set.
<TABLE>